Quantifying cumulative effectiveness of green stormwater infrastructure in improving water quality

Stormwater runoff is one of the main sources of pollution in streams and receiving water bodies of major cities. Green Stormwater Infrastructure (GSI) is a set of distributed stormwater best management practices that absorb excess water, filter out sediment and pollutants, and help recharge groundwater. Despite the increasing popularity of GSI as means of stormwater management, our knowledge of their cumulative performance is limited. In this research, we apply an empirical approach to study the effectiveness of GSI in improving the water quality of four major receiving water bodies of Seattle, Washington, at the watershed scale. We use a Bayesian structural time series model and synthetic control method to build counterfactual scenarios of water quality in absence of GSI implementation and estimate the causal impact of GSI on water quality. We use monthly time series data of water quality parameters (water temperature, dissolved oxygen, surface PAR, chlorophyll a, Secchi depth, pH, light transmission, and fecal coliform) in Seattle's urban watersheds from 2004 to 2017. We also use a set of nine control variables to estimate the counterfactual water quality parameters. Our findings show that GSI improve some water quality parameters such as chlorophyll a, light transmission and Secchi depth, but increase water temperature and decrease dissolved oxygen in some water bodies.